The handle http://hdl.handle.net/1887/45008 holds various files of this Leiden University dissertation
Author: Sala, Michiel
Title: MR and CT evaluation of cardiovascular risk in metabolic syndrome Issue Date: 2016-12-14
MR AND CT EVALUATION OF CARDIOVASCULAR RISK IN METABOLIC SYNDROME
Michiel Lodewijk Sala
ISBN: 978-94-6299-483-6
The cover shows Portrait of a Gentleman by Charles Mellin (1597-1649), who painted it around 1630. The person portrayed is possibly Alessandro del Borro, a Tuscan general. By courtesy of Gemäldegalerie, Staatliche Musee zu Berlin. Reproduced with permission.
© Copyright 2016, M.L. Sala, The Hague, The Netherlands
The copyright of the articles that have been published has been transferred to the respective journals. No parts of this thesis may be reproduced or transmitted in any form, by any means, without prior permission of the author.
CARDIOVASCULAR RISK IN METABOLIC SYNDROME
Proefschrift
ter verkrijging van
de graad van Doctor aan de Universiteit Leiden, op gezag van Rector Magnificus prof.mr. C.J.J.M. Stolker,
volgens het besluit van het College voor Promoties te verdedigen op woensdag 14 december 2016
klokke 11.15 uur
door
Michiel Lodewijk Sala
geboren te Utrecht in 1985
Promotor: Prof. dr. A. de Roos Co-promotores: Dr. L.J.M. Kroft
Dr. J. Van der Grond
Overige leden: Prof. dr. ir. B.P.F. Lelieveldt
Prof. dr. M.V. Huisman
Prof. dr. T. Leiner (UMC Utrecht)
Dr. B.K. Velthuis (UMC Utrecht)
Prof. dr. W.H. Backes (Maastricht UMC)
CHAPTER 1 General introduction and outline of the thesis 9
CHAPTER 2 Genetically determined prospect to become long-lived is associated with less abdominal fat and in particular less abdominal visceral fat in men
Sala ML, Roell B, van der Bijl, et al. Age and Ageing 2015 Jul;44(4):713-7
17
CHAPTER 3 Association of liver enzymes and computed tomography markers of liver steatosis with familial longevity
Sala ML, Kroft LJ, Roell B, et al. PLoS One, 2014 Mar14;9(3)e91085
29
CHAPTER 4 Liver fat assessed with CT relates to MRI markers of incipient brain injury in middle-aged to elderly persons with overweight Sala ML, van der Grond J, van Heemst D, et al. AJR Am J Roentgenol 2016 May 21;206(5):1087-92
49
CHAPTER 5 Aortic arch stiffness is associated with incipient brain injury in patients with hypertension
Sala ML, van den Berg-Huysmans A, van der Grond J, et al. Am J Hypertens 2016 Jun;29(6):705-12
65
CHAPTER 6 Microstructural brain tissue damage in metabolic syndrome
Sala ML, de Roos A, van den Berg-Huysmans A, et al. Diabetes Care 2014 Feb;37(2):493-500
83
CHAPTER 7 Association between changes in brain microstructure and cognition in older subjects at increased risk for vascular disease
Sala ML, de Roos A, Blauw GJ, et al. BMC Neurol 2015 Aug 7;15:133
101
CHAPTER 8 The effect of temporal resolution on the accuracy of aortic arch pulse wave velocity from velocity-encoded magnetic resonance imaging
Sala ML, de Roos A, van den Boogaard P, et al. Submitted
117
Summary and conclusions Nederlandse samenvatting en conclusies List of publications
Curriculum Vitae Dankwoord
133 137 143 145 147
CHAP TER 1
GENERAL INTRODUCTION
There is a global epidemic of overweight and obesity in many parts of the world. Body mass index (BMI, defined as bodyweight in kilograms divided by the square height in meters (kg/
m2)) is a commonly used measure to classify overweight and obesity in adults. According to the World Health Organization (WHO), a BMI greater than or equal to 25 is overweight; a BMI greater than or equal to 30 is obesity. In 2014, more than 1.8 billion adults were over- weight, and 600 million were obese. Worldwide obesity has more than doubled since 1980 (1).
Parallel to the increasing prevalence of obesity and physical inactivity, metabolic syndrome has become a major public health problem (2). In most countries throughout the world between 20%
and 30% of the adult population can be characterized as having metabolic syndrome (3-9).
Patients with metabolic syndrome are at essentially twice the risk for cardiovascular disease com- pared to those without the syndrome. Furthermore, it raises the risk for type 2 diabetes by approx- imately 5-fold (10,11). Metabolic syndrome is a systemic disease with a multifactorial pathogen- esis and consists of abdominal obesity, hyperglycemia, dyslipidemia (decreased high-density lipoprotein cholesterol and/or increased plasma triglycerides), and elevated blood pressure (9).
Presence of three of any of these factors suffices for the diagnosis of metabolic syndrome (12). Pa- tients with metabolic syndrome are a heterogeneous population with varying risk. Early identifi- cation of those patients with subclinical cardiovascular or cerebrovascular disease manifestation is highly relevant as organ damage might still be reversible (13-15). Imaging can be used for risk stratification and optimizing individual prevention and treatment strategies in patients with met- abolic syndrome. This thesis evaluates MR and CT imaging techniques for identifying risk factors and subclinical disease in metabolic syndrome, as is summarized in the following paragraphs.
Visceral adiposity and fatty liver
Abdominal obesity is the most prevalent feature of metabolic syndrome. It is generally accepted that excess intra-abdominal fat accumulation is a key correlate and perhaps driver of the health risk associated with overweight and obesity (16). Waist circumference is a simple and inex- pensive yet effective clinical measure of abdominal obesity (17). However, waist circumference cannot distinguish between abdominal subcutaneous (SAT) and visceral adipose tissue (VAT).
It is known that these fat depots are morphologically and functionally different, and metabolic disturbances are considered greater in visceral adiposity than in subcutaneous obesity (16-18).
In addition, it is increasingly recognized that excess visceral adiposity may be a marker of dys- functional SAT leading to fat accumulation at undesired sites including the liver (16). Increasing evidence suggests that accumulation of fat in the liver is another important determinant of the cardiometabolic complications of obesity (19). For example, it has been shown that fatty liver is associated with dyslipidemia and dysglycemia, also after adjusting for the amount of abdominal visceral fat (20). It is crucial to assess body fat distribution to understand the adverse effects of
Chapter 1 obesity as specific fat depots may predispose certain individuals to developing obesity related
illnesses. MRI and computed tomography (CT) are commonly used methods for distinguishing and quantifying abdominal fat compartments as well as assessing fatty liver. These imaging bio- markers may help in advanced risk stratification.
Aortic stiffness
Aortic stiffness is an independent cardiovascular risk factor (21). It has been shown that individ- uals with obesity are likely to have an increase in aortic stiffness, independent of blood pres- sure, age, and other potential confounding factors (22,23). Aortic stiffness may be an important phenomenon linking obesity to increased cardiovascular risk. Increased aortic stiffness results in deficient absorption of the pulse waves traveling through the vascular system (24). The resultant excessive pulsatile flow is transmitted to the periphery where it may cause damage in end organs such as the brain and kidney, because these organs have low-resistance vascular beds and are passively perfused at high flow throughout the cardiac cycle (24). Pulse wave velocity (PWV), a commonly used surrogate marker of aortic stiffness (25), is defined as the velocity of the systolic wave front propagating through the aorta. Velocity-encoded magnetic resonance imaging (VE- MRI) is a noninvasive and accurate technique for measuring PWV that has been well-validated against invasive pressure measurements (26). Of note, VE-MRI allows for the measurement of regional PWV, also in the aortic arch, whereas ultrasound measurements merely provide and es- timation of global aortic PWV (27). Evaluation of the aortic arch may be of particular importance as it has a distinct association with cerebral microvascular disease (28). Increased aortic arch stiffness integrates and reflects the long-term effects of all identified and currently unknown car- diovascular risk factors, and can be assessed at a stage when organ damage may be reversible (25). MRI of aortic arch stiffness provides functional information about vessel compliance that may help determine risk for cardiovascular or cerebrovascular disease (29).
Brain damage
Risk factors associated with metabolic syndrome may accelerate brain disease. MRI can be used to assess imaging evidence of cerebral small vessel disease including white matter hyperintensi- ties and brain atrophy, which in turn have been related to cognitive decline (30). In addition to these overt structural brain abnormalities, novel MRI techniques including magnetization transfer imaging (MTI) and diffusion tensor imaging (DTI) have been used to detect microstructural brain tissue damage that is not visible on conventional structural MRI (31,32). By using these novel imaging techniques, recent studies have shown that changes in brain tissue integrity can be de- tected in association with risk factors associated with metabolic syndrome (33-35). However, evidence of metabolic syndrome as a risk factor per se is limited (30). In addition, it is unknown whether brain tissue decline is present before MRI evidence of cerebral small vessel disease may become overt. To formulate treatment and prevention strategies, it is crucial to understand the
mechanisms underlying cerebral microvascular disease (29). Using an advanced MRI protocol for combined assessment of aortic arch stiffness and brain tissue integrity may provide insight in the mechanisms underlying subclinical brain disease.
MR and CT imaging techniques
Imaging techniques are available for identifying risk factors and subclinical dis- ease in metabolic syndrome. As mentioned before, it is crucial to assess body fat dis- tribution to understand the adverse effects of obesity. MR and CT can be used to dis- tinguish and quantify abdominal fat compartments as well as assessing fatty liver.
VE-MRI has been used as a noninvasive and accurate method for measuring PWV, which is a surrogate marker of arterial stiffness. MRI of aortic stiffness may help determine risk for cardio- vascular or cerebrovascular disease. DTI and MTI are relatively new imaging techniques that enable subclinical brain disease to be explored in more detail. DTI probes the direction and magnitude of water diffusion along the axons (36), whereas MTI probes the protons bound to large molecules such as myelin lipids and proteins (37,38). Both DTI and MTI have been used to investigate brain tissue microstructure in normal aging, different disease states, and in association with cardiovascular and metabolic risk factors (39-42).
OUTLINE OF THE THESIS
The studies in this thesis focus on MRI and CT evaluation of cardiovascular risk in metabolic syn- drome.
In chapter 2, we evaluate whether regional body fat distribution assessed by CT is different in association with longevity, which in turn has been related to an exceptionally healthy metabolic profile and low prevalence of cardiovascular disease. Chapter 3 describes the extent of liver steatosis in non-diabetic offspring of long-lived siblings and controls by evaluating liver enzymes in plasma and liver to spleen CT attenuation ratio as a measure of liver fat. Chapter 4 reports the association between liver steatosis assessed by CT and brain tissue integrity assessed by MTI in middle-aged to elderly persons. In chapter 5 the relation between aortic arch PWV assessed by VE-MRI and DTI measures of brain tissue integrity is investigated in hypertension patients.
Chapter 6 investigates the association between metabolic syndrome and brain tissue integrity assessed by MTI and DTI. In addition, independent associations between the individual meta- bolic syndrome factors and brain tissue integrity are reported. Chapter 7 shows the association between MTI measures of brain tissue integrity and cognitive test performance in older persons at increased risk for vascular disease. Chapter 8 describes the effect of temporal resolution on the accuracy of aortic arch PWV assessed by VE-MRI in healthy volunteers and patients referred for cardiac MRI.
Chapter 1
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Michiel L. Sala Boudewijn Rӧell Noortje van der Bijl Jeroen van der Grond Anton J. M. de Craen P. Eline Slagboom Rob van der Geest Albert de Roos Lucia J.M. Kroft
Age and Ageing 2015 Jul;44(4): 713-7
CHAP TER 2
become long-lived is associated with less abdominal fat and in particular less abdominal visceral fat in men
ABSTRACT
Objective: Familial longevity is marked by an exceptionally healthy metabolic profile and low prevalence of cardiometabolic disease observed already at middle age. We aim to investigate whether regional body fat distribution, which has previously shown to be associated with car- diometabolic risk, is different in offspring of long-lived siblings compared to controls.
Materials and Methods: Our institutional review board approved the study and all partic- ipants (n = 344, average age in years 65.6) gave written informed consent. Offspring (n = 175) of nonagenarian siblings were included. Their partners (n = 169) were enrolled as controls.
For abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) measure- ments, a single-slice 8.0 mm computed tomography (CT) acquisition was planned at the level of the 5th lumbar vertebra. In addition, participants underwent prospectively electrocardiogra- phy-triggered unenhanced volumetric CT of the heart. Abdominal VAT and SAT areas (cm2) and epicardial adipose tissue (EAT) volumes (mL) were acquired by semiautomated segmentation techniques. Linear regression analysis was performed adjusting for cardiovascular risk factors.
Results: Total abdominal fat areas were smaller in male offspring compared to controls (353.0 cm2 versus 382.9 cm2, p = 0.022). The association between low abdominal VAT areas in male offspring (149.7 cm2, versus 167.0 cm2 in controls, p = 0.043) attenuated after additional adjustment for diabetes (p = 0.078). Differences were not observed for females. EAT volumes were similar between offspring of long-lived siblings and controls.
Conclusion: Males who have genetically determined prospect to become long-lived have less abdominal fat and in particular less abdominal VAT compared to controls.
Chapter 2
INTRODUCTION
Fat distribution plays a major role in human health (1) (2). Offspring of long-lived siblings have a lower prevalence of diabetes and metabolic syndrome at middle age, and, among individuals without diabetes, are more glucose tolerant and insulin sensitive compared to controls (3). The aim of our study was to investigate whether fat distribution as measured by computed tomogra- phy is related to longevity by comparing distribution of abdominal visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (SAT), and epicardial adipose tissue (EAT) in offspring of nonagenarian siblings and controls.
MATERIALS AND METHODS Study population
Our institutional review board approved the study and all participants gave informed consent.
Offspring of long-lived siblings were recruited from the Leiden Longevity Study (4). In short, 421 long-lived families were enrolled in the study between 2002 and 2006 according to the following inclusion criteria: (1) there were at least two living siblings per family who fulfilled the age criteria and were willing to participate, (2) men had to be aged 89 years or older and women had to be aged 91 years or older and (3) the sib pairs had to have the same parents. In 2002, only 0.5% of long-lived men were aged ≥ 89 years, and only 0.5% of long-lived women aged ≥ 91 years. Ac- cordingly, siblings who meet these age criteria are even rarer and are estimated to represent far less than 0.1% of the long-lived population (4). Offspring of these long-lived siblings were included as they were shown to have a 30% lower mortality rate compared to the general population (4).
Their partners, who share the same geographical and socio-economic background, were enrolled as control group (4). There were no selection criteria on health or demographic characteristics.
For the current study, subjects were recruited from the offspring of the long-lived siblings and their spouses. In addition, their partners, who share the same geographical and socio-economic background, were enrolled as control group (4). There were no selection criteria on health or demographic characteristics. In total, 344 subjects comprising 175 offspring and 169 controls were included. Imaging was performed between September 2009 and December 2010. Within this time frame, blood samples were taken for the determination of nonfasting serum levels of glucose, triglycerides, and high-density lipoprotein (HDL) and total cholesterol. Information on body mass index (BMI) and smoking status was obtained from the study subjects. Information on medical history including presence myocardial infarction and diabetes was requested from the participants’ general practitioner (4). Hypertension was defined as use of antihypertensive medication as obtained from pharmacy records (5).
Data acquisition
Participants underwent prospectively electrocardiography (ECG)-triggered unenhanced volu- metric CT of the heart. All examinations were performed on a 320 multidetector-row computed tomography. (CT) scanner (Aquilion ONE, Toshiba Medical Systems, Otawara, Japan). The scan range was set between the carina and cardiac apex. Depending on the expected scan range, a 320 × 0.5 mm or 280 × 0.5 mm detector configuration was used. The preferred reconstruction phase was determined during a breath-hold exercise and set at 45% or 75% of the cardiac RR-cycle, depending on heart rate. Volumetric imaging was acquired during a single heartbeat during breath hold at inspiration. Scan parameters were: tube voltage 120 kV and tube current 200–400 mA, depending on participant’s size and shape (200 mA for small or thin participants, 250 mA for average size participants, and 300– 400 mA for large or obese participants).
Rotation time was 0.35 sec. Images were reconstructed with a slice thickness of 3 mm. Estimat- ed effective radiation dose varied between 0.8 and 2.4 mSv (mean, 1.2 mSv) for cardiac CT.
For abdominal fat measurement, a single-slice 8.0 mm acquisition was planned at the level of the 5th lumbar vertebra. All examinations were performed on a 320 multidetector-row computed tomography (CT) scanner (Aquilion ONE, Toshiba Medical Systems, Otawara, Japan). Scan parameters were: tube voltage 120 kV, tube current 310 mA, Rotation time: 0.5 sec. Imaging was performed during breath hold after expiration. Estimated radiation dose was 0.3 mSv for abdominal CT.
Abdominal adipose tissue
Data were processed by two research fellows (MS, BR) under direct supervision of a radiologist with 13 years of experience in cardiac and abdominal imaging (LK).
Analysis of abdominal VAT and SAT was performed with dedicated fat measurement software available at the CT scanner console. An automatic tool was used for recognizing fat. Predefined thresholds for adipose tissue were set from -150 to -30 Hounsfield Units (HU) (22). By manual outlining of the abdominal muscular wall (Figure 1), subcutaneous adipose tissue was separated from intraabdominal adipose tissue surrounding the internal abdominal organs, which includes intraperitoneal and retroperitoneal adipose tissue. Intraabdominal adipose tissue is referred to as abdominal VAT. Accordingly, total abdominal fat areas (cm2) as well as abdominal VAT and SAT areas (cm2) were calculated automatically. In addition, VAT / SAT ratio was calculated.
Chapter 2
Figure 1. Computed tomography abdominal images at the level of the 5th lumbar vertebra, 8.0 mm slice thickness. The red areas represent visceral fat and the blue areas represent subcutaneous fat. Left panel: 49-year old male control participant, BMI of 26, the visceral fat area measured 119.6 cm2 and the subcutaneous fat area 156.8 cm2. Right panel: 50-year old female offspring participant, BMI of 20.1, the visceral fat area measured 39.6 cm2 and the blue area subcutaneous fat area 150.2cm2.
Epicardial adipose tissue
For analysis of epicardial adipose tissue, QMASS research software (Medis, Leiden, the Netherlands) was used. Epicardial fat was defined as all adipose tissue located with- in the pericardium, measured from the lowest point of the left ventricular apex to the low- est point at the pulmonary trunk at its diversion into the left and right pulmonary artery. The pericardium was manually traced within each slice to determine a region of interest (Figure 2). Within the region of interest, adipose tissue was defined as pixels within a window lev- el of -190 to -30 HU (23). Accordingly, the area in squared centimetres that corresponds to the defined HU range was calculated automatically. The total EAT volume was then calcu- lated by the summation of all slides multiplied by the reconstructed slice thickness in cen- timetres, which was 0.3 cm. This allowed manual calculation of the EAT volume in millilitres.
Twenty scans were slightly incomplete, missing a maximum of one or two slices at the scan edges at either the apex or the pulmonary trunk. These scans were also included as the amount of adi- pose tissue in these missing slices was considered negligible.
Figure 2. Computed tomography image of the heart, 3mm slice thickness. Left panel: the heart with the pericardium. Middle panel: On each slice level, contours were manually drawn at the pericardium.
Right panel: Within the region of interest, the area in squared centimetres that corresponds to the defined Hounsfield unit range was calculated automatically (area in red). The total epicardial adipose tissue volume was then calculated by the summation of all slides multiplied by the reconstructed slice thickness in centimetres, which was 0.3 cm. In this participant, the amount of epicardial fat on this slice was 8.52 cm2. By multiplying with slice thickness of 0.3cm, representing 2.56 cm3 (or mL) of fat. Total epicardial fat volume by multiplying by numbers of slices was 99.38 mL.
Statistical analysis
All statistical analyses were performed separately for sexes. Differences in subject characteristics between offspring and controls were assessed using student’s t-test, Pearson chi-squared test, and linear regression analysis adjusting for age and BMI. Differences in regional adipose tissue distribution in offspring of long-lived and control subjects were assessed with linear regression analysis, adjusting for age, BMI, smoking, hypertension, diabetes and serum levels of glucose, high-density lipoprotein (HDL) cholesterol, and triglycerides. Analyses were repeated after ad- justing for age, BMI, smoking, and use of specific type of antihypertensive medication (diuretics, beta-blockers, ace-inhibitors, calcium antagonists). Difference in VAT/SAT ratio between sexes was assessed using the same models. Continuous variables were tested for normality and, if ap- propriate, logarithmically transformed and used in calculations (LnTriglycerides). Data are pre- sented as geometric means. Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS), version 20.0. P < 0.05 was considered statistically significant.
RESULTS
Subject characteristics are shown in table 1. Regional adipose tissue distribution in offspring of long-lived and control subjects is shown in table 2. In men, total abdominal fat areas were small- er in offspring compared to controls (353.0 cm2 versus 382.9 cm2, respectively, p = 0.022 in the fully adjusted model). Abdominal SAT areas were smaller in male offspring compared to controls, however the difference between the two groups did not reach statistical significance (p
= 0.070). Abdominal VAT areas were smaller in male offspring compared to controls after ad-
Chapter 2 justing for age, BMI, smoking, hypertension, and serum levels of glucose, high-density lipopro-
tein (HDL) cholesterol, and triglycerides (149.7 cm2 versus 167.0 cm2, respectively, p = 0.043) but after additionally adjusting for diabetes the association attenuated (p = 0.078). In subjects without diabetes (n = 326), abdominal VAT areas were smaller in male offspring compared to controls (149.6 cm2 versus 166.6 cm2, respectively, p = 0.042). Mean VAT/SAT ratio was 0.778 in males versus 0.483 in females (p < 0.001 in the fully adjusted model). There was no difference in VAT/SAT ratio between offspring and controls. EAT volume were similar in offspring of long- lived siblings and controls, in both males and females (table 2). Results did not change materially after adjusting analyses for use of specific type of antihypertensive medication (data not shown).
Table 1: Characteristics of the study population
Male (n = 167) Female (n= 177)
Offspring (n = 96)
Control
(n = 71) P-value Offspring (n = 79)
Control
(n = 98) P-value
Age† 66.0 (0.62) 67.8 (0.87) 0.098 65.6 (0.67) 63.7 (0.66) 0.047*
BMI† 26.9 (0.31) 26.5 (0.37) 0.478 26.0 (0.53) 26.7 (0.45) 0.319
Hypertension, n (%)‡ 16 (17%) 21 (29%) 0.047* 18 (23%) 18 (18%) 0.468
Myocard infarct, n (%)‡ 1 (1%) 3 (2%) 0.195 0 (0%) 0 (0%) N/A
Diabetes, n (%)‡ 3 (3%) 8 (11%) 0.038* 2 (3%) 5 (5%) 0.402
Glucose 5.76 ± 0.11 5.97 ± 0.14 0.239 5.70 ± 0.14 5.93 ± 0.12 0.197
Triglycerides 1.78 ± 1.1 1.72 ± 1.1 0.703 1.32 ± 1.1 1.43 ± 1.1 0.288
HDL-cholesterol 1.30 ± 0.04 1.22 ± 0.04 0.140 1.63 ± 0.05 1.55 ± 0.04 0.271 Total cholesterol 5.57 ± 0.12 5.46 ± 0.13 0.549 5.67 ± 0.15 5.67 ± 0.12 0.991
Diuretics, n (%)‡ 6 (6%) 3 (4%) 0.567 5 (6%) 2 (2%) 0.146
Beta blockers, n (%)‡ 5 (5%) 10 (14%) 0.047* 7 (9%) 13 (13%) 0.358
Ace-inhibitors, n (%)‡ 7 (7%) 15 (21%) 0.009* 9 (11%) 9 (9%) 0.629
Calcium antagonists, n (%)‡ 2 (2%) 7 (10%) 0.028* 10 (13%) 4 (4%) 0.036*
Values are mean±standard error. P values are from student’s t-test (†) and Pearson chi-square test (‡). For the variables glucose, triglycerides (mmol/L), HDL-cholesterol (mmol/L), and total cholesterol (mmol/L), linear regression analysis was performed, correcting for age, sex, and BMI. BMI, body mass index. HDL: high-density lipoprotein. * P-value < 0.05
Table 2: Adipose tissue distribution in offspring of long-lived and control subjects stratified to gender
Male Female
Offspring Control P-value Offspring Control P-value
TFA (cm2)
Model 1 351.0 ± 12.6 371.0 ± 14.7 0.304 371.7 ± 15.7 410.5 ± 14.1 0.069 Model 2 353.8 ± 10.3 386.5 ± 11.2 0.008* 375.4 ± 11.5 394 ± 10.3 0.120 Model 3 353.0 ± 17.3 382.9 ± 16.8 0.022* 399.2 ± 20.3 414.0 ± 19.8 0.228 SAT (cm2)
Model 1 204.2 ± 8.1 211.0 ± 9.4 0.584 264.0 ± 11.3 287.5 ± 10.1 0.124
Model 2 200.8 ± 7.3 215.2 ± 8.0 0.102 262.0 ± 8.8 272.3 ± 7.8 0.268
Model 3 184.1 ± 12.2 200.8 ± 11.9 0.070 255.5 ± 15.7 264.5 ± 15.4 0.344 VAT (cm2)
Model 1 146.8 ± 6.5 160.0 ± 7.6 0.188 107.7 ± 6.1 123.0 ± 5.4 0.064
Model 2 153.0 ± 6.5 171.3 ± 7.1 0.020* 113.4 ± 6.0 122.1 ± 5.3 0.169
Model 3 167.8 ± 10.7 182.1 ± 10.4 0.078 143.8 ± 10.1 149.6 ± 9.9 0.343 EAT (mL)
Model 1 110.0 ± 5.0 111.8 ± 5.8 0.920 95.7 ± 4.8 96.6 ± 4.3 0.889
Model 2 116.5 ± 5.6 120.2 ± 6.1 0.588 101.3 ± 5.6 98.1 ± 5.0 0.581
Model 3 127.4 ± 9.4 128.2 ± 9.1 0.907 110.2 ± 9.7 108.0 ± 9.4 0.714
Values are means ± standard error. P-values are from linear regression analysis, using different models.
Model 1: adjusted for age
Model 2: adjusted for age, BMI, and smoking
Model 3: model 2 + hypertension, diabetes, and serum levels of glucose, HDL cholesterol, and triglycerides.
TFA: total abdominal fat area; SAT: abdominal subcutaneous adipose tissue; VAT: abdominal visceral adipose tissue; EAT: epicardial adipose tissue. * P-value < 0.05
Chapter 2
DISCUSSION
Our data show that males with genetically determined prospect to become long-lived have less abdominal fat and less abdominal VAT in particular compared to controls.
Increased deposition of fat in the abdominal visceral compartment may cause dysregulation of adipokine production (6) which in turn may contribute to the initiation and progression of meta- bolic and cardiovascular complications of obesity including hypertension, metabolic syndrome and type 2 diabetes (2,6,7). Consistent with a potential link between abdominal VAT and health outcomes, in our study population, prevalence of hypertension (17% versus 29%) as well as type 2 diabetes (3% versus 11%) was significantly lower in male offspring of long-lived siblings compared to controls which is in accordance with previous findings in familial nonagenarians (3). Of note, after adjusting for diabetes we found that the association between low abdominal VAT areas and familial longevity attenuated. On the other hand, male offspring without diabetes were characterized by significantly lower abdominal VAT areas compared to controls. Offspring of long-lived siblings may thus be protected against development of type 2 diabetes by relatively low abdominal VAT deposition.
Fat distribution changes during life and there is a known gender difference in body fat distribution (8,9). One recent study used dual-energy X-ray absorptiometry to assess body composition in 250 healthy subjects aged 18 to 70 years. A slow increase in the amount of abdominal visceral fat, which was higher in males of all age groups compared to females, was observed until 51 years old in both sexes; in older decades, males grew progressively in the amount of abdominal visceral fat, while in females the amount of visceral fat remained steady (10). Increasing amounts of abdominal VAT with age have been related to age-linked deterioration in cardiometabolic risk profile (7,11). Our findings suggest that male offspring of long-lived siblings may have an advantageous “younger” fat distribution as would be expected by their chronological age, when compared to partners from the general population that served as controls.
In contrast to our observed difference in abdominal fat distribution in male offspring compared to controls, we found no differences in total abdominal fat and abdominal VAT between female offspring and controls. Abdominal VAT depots are relatively small in women compared to men (7,12-14) and it has been suggested that the tendency of men to accumulate abdominal VAT may be a key factor in predicting why obesity is much more hazardous in men than in women (7).
We found similar abdominal VAT/SAT ratios between offspring of long-lived siblings and con- trols. Although it has been suggested that VAT/SAT ratio may be a correlate of cardiometabolic risk independent of absolute fat volumes (15,16), others have shown that abdominal VAT is a stronger correlate of cardiometabolic risk than the VAT/SAT ratio (15,17). It has been noted that
abdominal VAT may be a marker of fat deposition at undesired sites such as the liver (6,7). One recent study showed that the extent of liver steatosis, assessed by liver enzymes and computed tomography, is similar between offspring of long-lived siblings and controls (18). In line with this notion, it has been suggested that familial longevity is marked by enhanced peripheral but not hepatic insulin sensitivity (19).
We found no differences in EAT deposition between offspring and controls which may indicate that abdominal VAT rather than EAT has systemic metabolic effects associated with prospect to become long-lived (20).
Because of the known differences in fat distribution between men and women, analysis were separately performed for sexes. Therefore, offspring of long-lived siblings could not be directly compared to their own partners who served as controls. However, the overall study group was environmentally matched, which is unique in study design. While single-slice images for abdom- inal VAT measurements are often used in research studies to limit radiation exposure, it should be noted that they may be less accurate than volumetric analysis (21). Due to the observational nature it is not possible to infer causality from our study.
In conclusion, males who have genetically determined prospect to become long-lived have less abdominal fat and less abdominal VAT in particular compared to controls.
Chapter 2
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Michiel L. Sala Lucia J.M. Kroft Boudewijn Rӧell Jeroen van der Grond P. Eline Slagboom Simon P. Mooijaart Albert de Roos Diana van Heemst
PLoS One 2014 Mar 14;9(3)e91085
CHAP TER 3
tomography markers of liver steatosis with familial longevity
ABSTRACT
Objective: Familial longevity is marked by enhanced peripheral but not hepatic insulin sensi- tivity. The liver has a critical role in the pathogenesis of hepatic insulin resistance. Therefore we hypothesized that the extent of liver steatosis would be similar between offspring of long-lived siblings and control subjects. To test our hypothesis, we investigated the extent of liver steatosis in non-diabetic offspring of long-lived siblings and age-matched controls by measuring liver enzymes in plasma and liver fat by computed tomography (CT).
Materials and Methods: We measured nonfasting alanine transaminase (ALT), aspartate aminotransferase (AST), and ϒ-glutamyl transferase (GGT) in 1625 subjects (736 men, mean age 59.1 years) from the Leiden Longevity Study, comprising offspring of long-lived siblings and partners thereof. In a random subgroup, fasting serum samples (n=230) were evaluated and CT was performed (n=268) for assessment of liver-spleen (L/S) ratio and the prevalence of moderate-to-severe non-alcoholic fatty liver disease (NAFLD). Linear mixed model analysis was performed adjusting for age, gender, body mass index, smoking, use of alcohol and hepatotoxic medication, and correlation of sibling relationship.
Results: Offspring of long-lived siblings had higher nonfasting ALT levels as compared to control subjects (24.3 mmol/L versus 23.2 mmol/L, p= 0.03), while AST and GGT levels were similar between the two groups. All fasting liver enzyme levels were similar between the two groups. CT L/S ratio and prevalence of moderate-to-severe NAFLD was similar between groups (1.12 vs 1.14, p=0.25 and 8% versus 8%, p=0.91, respectively).
Conclusions: Except for nonfasting levels of ALT, which were slightly higher in the offspring of long-lived siblings compared to controls, no differences were found between groups in the extent of liver steatosis, as assessed with liver biochemical tests and CT. Thus, our data indicate that the extent of liver steatosis is similar between offspring of long-lived siblings and control subjects.
Chapter 3
INTRODUCTION
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver dis- ease in Western countries and is associated with metabolic risk factors such as obesi- ty, diabetes mellitus, and dyslipedimia (1). NAFLD is prevalent in more than one-third of the elderly (2), while prevalence may increase up to 69% in type 2 diabetes patients (3).
Hepatocyte dysfunction due to liver fat accumulation may interfere with insulin action and cause hepatic insulin resistance (4). Accordingly, the liver enzymes ϒ-glutamyl transfer- ase (GGT) and alanine aminotransferase (ALT) correlate with liver fat content, and have been shown to predict impaired glucose metabolism and type 2 diabetes mellitus inci- dence (5). On the other hand, secondary to insulin resistance, prolonged compensato- ry hyperinsulinemia may lead to the development of NAFLD (6). From this point of view, NAFLD may be a consequence rather than a cause of age related insulin resistance.
Offspring of long-lived siblings exhibit an exceptional healthy glucose metabolism in middle age, including preservation of insulin sensitivity and increased glucose tolerance (7,8). We have previously shown that these subjects had a higher insulin-mediated glucose disposal rate (periph- eral insulin sensitivity), while the capacity of insulin to suppress endogenous glucose production (hepatic insulin sensitivity) was not different as compared to controls (8). In line with enhanced peripheral glucose disposal, we have previously shown that lipid accumulation within muscle cells was lower in offspring of nonagenarian siblings as compared to controls (8,9). Likewise, given the correlation between insulin resistance and liver steatosis (10), it can be questioned whether there is an association between the extent of liver steatosis and the healthy metabolic profile observed in familial longevity. One previous study found that nonfasting serum triglycer- ide levels were lower in offspring of long-lived siblings as compared to controls, albeit only in women (11). Accordingly, while serum triglyceride levels correlate with liver fat content (12), this may suggest that the extent of liver steatosis is lower in offspring of long-lived siblings. However, triglyceride levels were determined in nonfasting samples, so results might have potentially been confounded by differences in food intake between groups. Moreover, it was previously shown that familial longevity is marked by enhanced peripheral but not hepatic insulin sensitivity (8).
Based on these considerations, we hypothesized that the extent of liver steatosis would be similar between offspring of long-lived siblings and control subjects. To test our hypothesis, we evalu- ated liver biochemical tests (aspartate aminotransferase [AST], ALT, and GGT) and computed tomography markers of liver steatosis in the non-diabetic offspring of long-lived siblings and age-matched controls.
MATERIALS AND METHODS Study Subjects
The Medical Ethical Committee of the Leiden University Medical Center approved the study, and written informed consent was obtained from all subjects according to the Declaration of Helsinki.
Subjects were included from the Leiden Longevity Study, which has been described in more de- tail elsewhere (13). In short, 421 Dutch Caucasian families were enrolled in the study between 2002 and 2006 based on the following inclusion criteria: (1) there were at least two living siblings per family, who fulfilled the age criteria and were willing to participate, (2) men had to be aged ≥ 89 years and women had to be aged ≥ 91 years and (3) the sib pairs had to have the same parents. In 2002, only 0.5% of Dutch men were aged 89 and older, and only 0.5%
of Dutch women aged 91 and older. Accordingly, siblings who meet these age criteria are even rarer and are estimated to represent far less than 0.1% of the population in the Netherlands (14). Offspring of these long-lived siblings were included as they were shown to have a 35%
lower mortality rate compared to the general population. Their partners, who share the same socio-economic and geographical background, were enrolled as age-matched control group (13). Accordingly, there were no selection criteria on health or demographic characteristics.
In total, 2415 subjects comprising1671 offspring and 744 partners are included in the Leiden Longevity Study. For the current study, additional information was collected, including self-report- ed information on height, weight, alcohol intake and smoking habits. Information on past medical history was obtained from the participants’ treating physicians. Subjects with diabetes (65 off- spring and 53 partners) were excluded. Subjects were regarded as having diabetes if they had nonfasting glucose levels >11.0 mmol/L, a previous medical history of diabetes and/or used glu- cose lowering agents. Of the remaining 2297 subjects, we excluded subjects with plasma levels more than threefold higher than the upper reference limit for GGT (18 offspring, 14 partners) or ALT (0 offspring, 1 partner). For the remaining subjects, all plasma AST levels were within the ref- erence range. For the remaining 2264 subjects, plasma samples were not available for 35 sub- jects (25 offspring, 10 partners) and serum data on GGT or ALT were not available for 46 sub- jects (32 offspring, 14 partners). In total 8 subjects (8 offspring, 0 partners) were excluded based on presence of chronic hepatitis (n=4), liver steatosis (n=3, confirmed by ultrasound), or liver metastasis (n=1) in past medical history. Accordingly, information on medication was lacking for 238 subjects (173 offspring, 65 partners), data on alcohol intake was missing for 293 subjects (214 offspring, 79 partners), information on smoking was lacking for 12 subjects (11 offspring, 1 partner), and information on BMI was lacking for 7 subjects (3 offspring, 4 partners). Hence, in total 1625 subjects (1122 offspring and 503 partners) were selected for the current analyses.
From the cohort of 2415 subjects, a subgroup of 234 was previously recruited, from which fasting serum samples were obtained and who participated in an oral glucose tolerance test (OGTT) (7). From this group of 234 subjects, 4 subjects were excluded because GGT lev-
